A new research paper investigates why transformer language models, like GPT-2, struggle with "impossible" languages that humans can acquire. The study found that while these models show some sensitivity to grammaticality, they exhibit significant deficiencies in generating high-quality, longer sentences. This suggests that generative failures, rather than grammatical insensitivity, may be the primary reason these models cannot process such unnatural languages. AI
IMPACT Suggests limitations in current transformer architectures for handling complex linguistic structures, potentially guiding future model development.
RANK_REASON Research paper published on arXiv detailing findings about transformer language models.
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